Telling Stories with Data

Resources

There is no formal textbook in this course.

• R Cookbook: https://rc2e.com/

• R Graphics Cookbook: https://r-graphics.org/

• R for Data Science: https://r4ds.had.co.nz

• A Course in Machine Learning: https://ciml.info/

• Understanding Machine Learning – From Theory to Algorithms: https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/index.html

• Hands-on Machine Learning: https://bradleyboehmke.github.io/HOML/

• Telling Stories with Data: https://tellingstorieswithdata.com/

• Active Statistics: https://avehtari.github.io/ActiveStatistics/

• Learning Statistics with R: https://learningstatisticswithr.com/

• Big Book of R: https://www.bigbookofr.com/

Assignments

Participation: Attendance will be taken via coding exercises. You must attend greater than 90% of classes for full credit.

Weekly Lab Assignments: 12 total. Friday to Monday. Complete in R. Turn in via Github.

Practicals: Midterm + Final. Comprehensive. 1 week to complete. Complete in R. Turn in via Github.

Graduate Students only

Research Project: a project proposal, project code, statistical justification, and a final presentation [Final Exam Day].

Course Specific Information

Collaboration and Group Work

Learning is a group endeavor. Especially statistics! I encourage each of you to discuss lectures outside of class, use each other when there is a difficult topic, and work on labs and assignments together. That said, your work is your work, if you are going to collaborate, then each of you must turn in your own work.

AI Usage

While AI is a powerful tool, it does not supplement your own work and learning. AI software may assist you – or even give you the answers to – how to code things. The beauty of coding is that there are millions of approaches to come to the same solution. I therefore, cannot oversee your personal use of AI in this class. However, I encourage you to exercise your ability to train your search-engine algorithms (i.e., Google it) and consult with your peers instead.